The rapid development of wireless devices and their ever-improving mobile capabilities have enabled users to depend on them for increasing numbers of applications while the devices can obtain a wide variety of personal information. Users of such devices are increasingly able to capture contextual information about their environment, their interactions, and themselves on various platforms. These platforms include, but are not limited to, mobile computing/communication devices (e.g., PDAs, phones, MIDs), fixed and portable computing devices (laptops, desktops, and set-top-boxes), and cloud computing services and platforms. Both raw context and profiles derived from this context have a potentially high value to the user, if the user can properly manage and share this information with service providers. Service providers may use this information to better tailor offers to the user, to better understand their customers, or to repackage and sell (or otherwise monetize).
The user potentially stands to benefit through a better service experience or through a specific incentive. The user's ability to leverage this context is currently limited in the following ways: there is no automated way to share, combine, or integrate context across platforms owned by the same user; there is no automated and/or standardized way for the user to share this context with service providers, with or without compensation; and there is no simple mechanism for controlling access to context.
Currently this type of information may be collected and monetized in a variety of ways:                Context is collected by cloud service providers, via web logs, credit card traces, and other artifacts of day-to-day life. Such context might identify life events or in-market interests. The user often has no knowledge or control over this information and gains nothing in its use.        Context is collected automatically on the user's platforms and delivered back to a service provider. For example, set-top boxes often collect information about TV viewing patterns, and cell phones often track location.        Users may explicitly share context by filling out a survey about their interests, demographics, or personal history.        
In the above examples, users are not always aware their context is being collected. They may or may not be given the opportunity to opt-out and in most cases, the user will receive no compensation for their context. Also, users do not have a way to allow the gathering of their information anonymously. Thus, a strong need is present for techniques for anonymized monetizing of context information.
It will be appreciated that for simplicity and clarity of illustration, elements illustrated in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements are exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals have been repeated among the figures to indicate corresponding or analogous elements.